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GitHub Skill

backend-fastapi

Documentation for the FastAPI backend, endpoints, and dependency injection.

aiskillstore☆ 124 Repository stars6 Repository forks

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

View source ↗Guides

Skill instructions

Backend Architecture (FastAPI)

Overview

The backend is a FastAPI application located in backend/. It powers the chatbot and RAG functionality.

Entry Point

  • File: backend/main.py
  • Run: uvicorn backend.main:app --reload (or via npm run dev)
  • Port: Defaults to 8000.

Endpoints

POST /api/chat
  • Purpose: Main RAG chat endpoint.
  • Input: ChatRequest (query, history, user_context).
  • Process:
    1. Embed query.
    2. Search Qdrant (search_qdrant).
    3. Build prompt (build_rag_prompt).
    4. Generate Agent response.
  • Output: ChatResponse (answer, contexts).
POST /api/ask-selection
  • Purpose: Targeted Q&A on selected text.
  • Input: AskSelectionRequest (question, selected_text).
  • Process:
    1. Validates selection length.
    2. Builds selection-specific prompt.
    3. specific Agent instructions.

Dependencies & Utils

  • backend/utils/config.py: Qdrant initialization.
  • backend/utils/helpers.py: Embedding and Prompt building logic.
  • backend/models.py: OpenAI/Gemini client setup.

Environment Variables

  • GEMINI_API_KEY: For LLM and Embeddings.
  • QDRANT_URL, QDRANT_API_KEY: Vector DB connection.

Examples

Read the usage examples in the source repository.

View source ↗

Source history

Indexed on
2026-01-20
Index updated
2026-01-28
Branch
main

Index and sync dates describe this directory. View the source repository for code history.

GitHub ↗

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